{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:20:57.319236100Z",
     "start_time": "2024-03-14T13:20:57.202840700Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from collections import Counter\n",
    "\n",
    "#设置为seaborn风格\n",
    "sns.set()\n",
    "#不显示警告\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  #显示中文\n",
    "plt.rcParams['axes.unicode_minus'] = False  #用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "86b5e8fea1bbf3ce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:20:57.568278600Z",
     "start_time": "2024-03-14T13:20:57.319236100Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "users = pd.read_csv('./user_expand.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37558e6641f84cd8",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## 点击与下单 将time转化为星期和小时"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a412528ed431cd5a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:20:57.584042700Z",
     "start_time": "2024-03-14T13:20:57.569278700Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_ID</th>\n",
       "      <th>user_level</th>\n",
       "      <th>first_order_month</th>\n",
       "      <th>plus</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>education</th>\n",
       "      <th>city_level</th>\n",
       "      <th>purchase_power</th>\n",
       "      <th>is_click</th>\n",
       "      <th>is_order</th>\n",
       "      <th>order_click_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000089d6a6</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-08</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000babd1f</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03</td>\n",
       "      <td>0</td>\n",
       "      <td>U</td>\n",
       "      <td>-1</td>\n",
       "      <td>U</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000bc018b</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-06</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>-1</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000d0e5ab</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-06</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0000dce472</td>\n",
       "      <td>3</td>\n",
       "      <td>2012-08</td>\n",
       "      <td>1</td>\n",
       "      <td>U</td>\n",
       "      <td>-1</td>\n",
       "      <td>U</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.611111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_ID  user_level first_order_month  plus gender  age marital_status  \\\n",
       "0  000089d6a6           1           2017-08     0      F    3              S   \n",
       "1  0000babd1f           1           2018-03     0      U   -1              U   \n",
       "2  0000bc018b           3           2016-06     0      F   -1              M   \n",
       "3  0000d0e5ab           3           2014-06     0      M    3              M   \n",
       "4  0000dce472           3           2012-08     1      U   -1              U   \n",
       "\n",
       "   education  city_level  purchase_power  is_click  is_order  \\\n",
       "0          3           4               3      10.0       8.0   \n",
       "1         -1          -1              -1       2.0       2.0   \n",
       "2          3           2               3      16.0       8.0   \n",
       "3          3           2               2       2.0       2.0   \n",
       "4         -1          -1              -1      18.0      11.0   \n",
       "\n",
       "   order_click_ratio  \n",
       "0           0.800000  \n",
       "1           1.000000  \n",
       "2           0.500000  \n",
       "3           1.000000  \n",
       "4           0.611111  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d0435495453a339a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:21:13.703421300Z",
     "start_time": "2024-03-14T13:20:57.585042600Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "clor = pd.read_csv('./CL_OR.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6ebf3e24c93ca3eb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:21:13.721963800Z",
     "start_time": "2024-03-14T13:21:13.703421300Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sku_ID                                 a234e08c57\n",
       "user_ID                                4c3d6d10c2\n",
       "request_time                  2018-03-01 23:57:53\n",
       "channel                                    wechat\n",
       "order_ID                                      NaN\n",
       "order_date                                    NaN\n",
       "order_time                                    NaN\n",
       "quantity                                      NaN\n",
       "type                                          NaN\n",
       "promise                                       NaN\n",
       "original_unit_price                           NaN\n",
       "final_unit_price                              NaN\n",
       "direct_discount_per_unit                      NaN\n",
       "quantity_discount_per_unit                    NaN\n",
       "bundle_discount_per_unit                      NaN\n",
       "coupon_discount_per_unit                      NaN\n",
       "gift_item                                     NaN\n",
       "dc_ori                                        NaN\n",
       "dc_des                                        NaN\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clor.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "52b53189b1d4bb67",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:21:52.752456900Z",
     "start_time": "2024-03-14T13:21:13.722972700Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "clor['request_time'] = pd.to_datetime(clor['request_time'])\n",
    "clor['click_week'] = clor['request_time'].apply(lambda x: x.weekday() + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "33e7e32acfff9f8e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:21:52.767976200Z",
     "start_time": "2024-03-14T13:21:52.754460900Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sku_ID                                 a234e08c57\n",
       "user_ID                                4c3d6d10c2\n",
       "request_time                  2018-03-01 23:57:53\n",
       "channel                                    wechat\n",
       "order_ID                                      NaN\n",
       "order_date                                    NaN\n",
       "order_time                                    NaN\n",
       "quantity                                      NaN\n",
       "type                                          NaN\n",
       "promise                                       NaN\n",
       "original_unit_price                           NaN\n",
       "final_unit_price                              NaN\n",
       "direct_discount_per_unit                      NaN\n",
       "quantity_discount_per_unit                    NaN\n",
       "bundle_discount_per_unit                      NaN\n",
       "coupon_discount_per_unit                      NaN\n",
       "gift_item                                     NaN\n",
       "dc_ori                                        NaN\n",
       "dc_des                                        NaN\n",
       "click_week                                      4\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clor.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c055584abd96e6a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:22:25.113061600Z",
     "start_time": "2024-03-14T13:21:52.767976200Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "clor['click_hour'] = clor['request_time'].apply(lambda x: x.hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37782a6aeb588b6c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:22:25.128387500Z",
     "start_time": "2024-03-14T13:22:25.113061600Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sku_ID                                 a234e08c57\n",
       "user_ID                                4c3d6d10c2\n",
       "request_time                  2018-03-01 23:57:53\n",
       "channel                                    wechat\n",
       "order_ID                                      NaN\n",
       "order_date                                    NaN\n",
       "order_time                                    NaN\n",
       "quantity                                      NaN\n",
       "type                                          NaN\n",
       "promise                                       NaN\n",
       "original_unit_price                           NaN\n",
       "final_unit_price                              NaN\n",
       "direct_discount_per_unit                      NaN\n",
       "quantity_discount_per_unit                    NaN\n",
       "bundle_discount_per_unit                      NaN\n",
       "coupon_discount_per_unit                      NaN\n",
       "gift_item                                     NaN\n",
       "dc_ori                                        NaN\n",
       "dc_des                                        NaN\n",
       "click_week                                      4\n",
       "click_hour                                     23\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clor.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b06d9f55d17bd1dc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:22:58.833004700Z",
     "start_time": "2024-03-14T13:22:25.129386900Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "clor['order_time'] = pd.to_datetime(clor['order_time'])\n",
    "clor['order_week'] = clor['order_time'].apply(lambda x: x.weekday() + 1)\n",
    "clor['order_hour'] = clor['order_time'].apply(lambda x: x.hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c0722e9e3549588",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:22:58.848244Z",
     "start_time": "2024-03-14T13:22:58.833004700Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sku_ID                                 a234e08c57\n",
       "user_ID                                4c3d6d10c2\n",
       "request_time                  2018-03-01 23:57:53\n",
       "channel                                    wechat\n",
       "order_ID                                      NaN\n",
       "order_date                                    NaN\n",
       "order_time                                    NaT\n",
       "quantity                                      NaN\n",
       "type                                          NaN\n",
       "promise                                       NaN\n",
       "original_unit_price                           NaN\n",
       "final_unit_price                              NaN\n",
       "direct_discount_per_unit                      NaN\n",
       "quantity_discount_per_unit                    NaN\n",
       "bundle_discount_per_unit                      NaN\n",
       "coupon_discount_per_unit                      NaN\n",
       "gift_item                                     NaN\n",
       "dc_ori                                        NaN\n",
       "dc_des                                        NaN\n",
       "click_week                                      4\n",
       "click_hour                                     23\n",
       "order_week                                    NaN\n",
       "order_hour                                    NaN\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clor.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f8a91538623d6ed",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "### 周一到周日点击、下单用户个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e4fa464f9a59c06",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:23:07.394342500Z",
     "start_time": "2024-03-14T13:22:58.848244Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "user_order_week = clor.groupby('order_week')['user_ID'].nunique()\n",
    "user_order_week = user_order_week.to_frame().reset_index()\n",
    "user_order_week.columns = ['order_week', 'user_order_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c7cc95fa19631614",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:23:07.409354400Z",
     "start_time": "2024-03-14T13:23:07.394342500Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_week</th>\n",
       "      <th>user_order_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>45713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>47934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>54566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>77624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>59903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>55191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>42504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_week  user_order_num\n",
       "0         1.0           45713\n",
       "1         2.0           47934\n",
       "2         3.0           54566\n",
       "3         4.0           77624\n",
       "4         5.0           59903\n",
       "5         6.0           55191\n",
       "6         7.0           42504"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_order_week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8856a7634ffcf481",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:23:17.277399400Z",
     "start_time": "2024-03-14T13:23:07.407354700Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "user_click_week = clor.groupby('click_week')['user_ID'].nunique()\n",
    "user_click_week = user_click_week.to_frame().reset_index()\n",
    "user_click_week.columns = ['click_week', 'user_click_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f71f2c1bed8f75c7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:23:17.300495Z",
     "start_time": "2024-03-14T13:23:17.276400Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click_week</th>\n",
       "      <th>user_click_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>476307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>495276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>543988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>708251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>580857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>559282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>445596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   click_week  user_click_num\n",
       "0           1          476307\n",
       "1           2          495276\n",
       "2           3          543988\n",
       "3           4          708251\n",
       "4           5          580857\n",
       "5           6          559282\n",
       "6           7          445596"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_click_week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1de1b5bb9947404",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:23:17.331068100Z",
     "start_time": "2024-03-14T13:23:17.290971700Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['click_week', 'user_click_num'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(user_click_week.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9a3a5ef885a2f31c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:23:17.510232400Z",
     "start_time": "2024-03-14T13:23:17.308019Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 条形宽度\n",
    "bar_width = 0.2\n",
    "# 透明度\n",
    "opacity = 0.4\n",
    "\n",
    "# 计算每个条形图的位置\n",
    "positions_order = np.arange(len(user_order_week['order_week']))\n",
    "positions_click = [x + bar_width for x in positions_order]\n",
    "\n",
    "# 星期-下单，使用 positions_order 调整位置\n",
    "plt.bar(positions_order, user_order_week['user_order_num'], bar_width, alpha=opacity, color='c', label='Order')\n",
    "\n",
    "# 星期-点击，使用 positions_click 调整位置\n",
    "plt.bar(positions_click, user_click_week['user_click_num'], bar_width, alpha=opacity, color='r', label='Click')\n",
    "\n",
    "# 添加折线图\n",
    "plt.plot(positions_order, user_order_week['user_order_num'], color='b', marker='o', label='Average Order')\n",
    "plt.plot(positions_click, user_click_week['user_click_num'], color='y', marker='x', label='Average Click')\n",
    "\n",
    "# 设置 x 轴标签和标题\n",
    "plt.xlabel('Week Day')\n",
    "plt.ylabel('Users Number')\n",
    "plt.title('A Week Purchase Table with Trends')\n",
    "\n",
    "# 设置 x 轴刻度标签位置和标签内容\n",
    "plt.xticks([p + bar_width / 2 for p in positions_order], user_order_week['order_week'])\n",
    "\n",
    "# 其他设置\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ab5e43fe1a20e5",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "### 每天中的点击、下单个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dd40dbc38b3d3ca9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:23:26.483822Z",
     "start_time": "2024-03-14T13:23:17.511343400Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_hour</th>\n",
       "      <th>user_order_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.0</td>\n",
       "      <td>14180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>20715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.0</td>\n",
       "      <td>28248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.0</td>\n",
       "      <td>24381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12.0</td>\n",
       "      <td>22849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.0</td>\n",
       "      <td>25544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.0</td>\n",
       "      <td>25612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15.0</td>\n",
       "      <td>24990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16.0</td>\n",
       "      <td>22466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.0</td>\n",
       "      <td>18644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18.0</td>\n",
       "      <td>16029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19.0</td>\n",
       "      <td>17096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20.0</td>\n",
       "      <td>21859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.0</td>\n",
       "      <td>25750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22.0</td>\n",
       "      <td>27671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.0</td>\n",
       "      <td>19606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    order_hour  user_order_num\n",
       "0          0.0            9843\n",
       "1          1.0            3774\n",
       "2          2.0            1605\n",
       "3          3.0            1009\n",
       "4          4.0             854\n",
       "5          5.0            1232\n",
       "6          6.0            3671\n",
       "7          7.0            8138\n",
       "8          8.0           14180\n",
       "9          9.0           20715\n",
       "10        10.0           28248\n",
       "11        11.0           24381\n",
       "12        12.0           22849\n",
       "13        13.0           25544\n",
       "14        14.0           25612\n",
       "15        15.0           24990\n",
       "16        16.0           22466\n",
       "17        17.0           18644\n",
       "18        18.0           16029\n",
       "19        19.0           17096\n",
       "20        20.0           21859\n",
       "21        21.0           25750\n",
       "22        22.0           27671\n",
       "23        23.0           19606"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_order_hour = clor.groupby('order_hour')['user_ID'].nunique()\n",
    "user_order_hour = user_order_hour.to_frame().reset_index()\n",
    "user_order_hour.columns = ['order_hour', 'user_order_num']\n",
    "user_order_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cd7a7209551cb971",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:23:40.924235100Z",
     "start_time": "2024-03-14T13:23:26.483822Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click_hour</th>\n",
       "      <th>user_click_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>136929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>54002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>26776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>18248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>17029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>24797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>71354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>132477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>193627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>255478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>305617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>270722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>264580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>292180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>292169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>283005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>263626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>224797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>199990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>221279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>265229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>311742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>321178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>237328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    click_hour  user_click_num\n",
       "0            0          136929\n",
       "1            1           54002\n",
       "2            2           26776\n",
       "3            3           18248\n",
       "4            4           17029\n",
       "5            5           24797\n",
       "6            6           71354\n",
       "7            7          132477\n",
       "8            8          193627\n",
       "9            9          255478\n",
       "10          10          305617\n",
       "11          11          270722\n",
       "12          12          264580\n",
       "13          13          292180\n",
       "14          14          292169\n",
       "15          15          283005\n",
       "16          16          263626\n",
       "17          17          224797\n",
       "18          18          199990\n",
       "19          19          221279\n",
       "20          20          265229\n",
       "21          21          311742\n",
       "22          22          321178\n",
       "23          23          237328"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_click_hour = clor.groupby('click_hour')['user_ID'].nunique()\n",
    "user_click_hour = user_click_hour.to_frame().reset_index()\n",
    "user_click_hour.columns = ['click_hour', 'user_click_num']\n",
    "user_click_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "40b38735af757fe4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:23:41.145078600Z",
     "start_time": "2024-03-14T13:23:40.926236600Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 条形宽度\n",
    "bar_width = 0.2\n",
    "# 透明度\n",
    "opacity = 0.4\n",
    "\n",
    "# 计算每个条形图的位置\n",
    "positions_order = np.arange(len(user_order_hour['order_hour']))\n",
    "positions_click = [x + bar_width for x in positions_order]\n",
    "\n",
    "# 星期-下单，使用 positions_order 调整位置\n",
    "plt.bar(positions_order, user_order_hour['user_order_num'], bar_width, alpha=opacity, color='c', label='Order')\n",
    "\n",
    "# 星期-点击，使用 positions_click 调整位置\n",
    "plt.bar(positions_click, user_click_hour['user_click_num'], bar_width, alpha=opacity, color='r', label='Click')\n",
    "\n",
    "# 添加折线图\n",
    "plt.plot(positions_order, user_order_hour['user_order_num'], color='b', marker='o', label='Average Order')\n",
    "plt.plot(positions_click, user_click_hour['user_click_num'], color='y', marker='x', label='Average Click')\n",
    "\n",
    "# 设置 x 轴标签和标题\n",
    "plt.xlabel('Hour in Day')\n",
    "plt.ylabel('Users Number')\n",
    "plt.title('A Week Purchase Table with Trends')\n",
    "\n",
    "# 设置 x 轴刻度标签位置和标签内容\n",
    "plt.xticks([p + bar_width / 2 for p in positions_order], user_order_hour['order_hour'], rotation=45)\n",
    "\n",
    "# 其他设置\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3b30ca21d9f06453",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:24:54.400246900Z",
     "start_time": "2024-03-14T13:23:41.142078100Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# 保存csv\n",
    "clor.to_csv('CL_OR.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1757fcc955323f4",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## 月中的每天购买数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2a312a443800b3a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:25:14.637898200Z",
     "start_time": "2024-03-14T13:24:54.401252300Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "clor = pd.read_csv('./CL_OR.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b3ca6c32afa8ec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:25:14.656972700Z",
     "start_time": "2024-03-14T13:25:14.640408600Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sku_ID</th>\n",
       "      <th>user_ID</th>\n",
       "      <th>request_time</th>\n",
       "      <th>channel</th>\n",
       "      <th>order_ID</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_time</th>\n",
       "      <th>quantity</th>\n",
       "      <th>type</th>\n",
       "      <th>promise</th>\n",
       "      <th>...</th>\n",
       "      <th>quantity_discount_per_unit</th>\n",
       "      <th>bundle_discount_per_unit</th>\n",
       "      <th>coupon_discount_per_unit</th>\n",
       "      <th>gift_item</th>\n",
       "      <th>dc_ori</th>\n",
       "      <th>dc_des</th>\n",
       "      <th>click_week</th>\n",
       "      <th>click_hour</th>\n",
       "      <th>order_week</th>\n",
       "      <th>order_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a234e08c57</td>\n",
       "      <td>4c3d6d10c2</td>\n",
       "      <td>2018-03-01 23:57:53</td>\n",
       "      <td>wechat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6449e1fd87</td>\n",
       "      <td>-</td>\n",
       "      <td>2018-03-01 16:13:48</td>\n",
       "      <td>wechat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09b70fcd83</td>\n",
       "      <td>2791ec4485</td>\n",
       "      <td>2018-03-01 22:10:51</td>\n",
       "      <td>wechat</td>\n",
       "      <td>e4874e2a00</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>2018-03-01 14:08:33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09b70fcd83</td>\n",
       "      <td>eb0718c1c9</td>\n",
       "      <td>2018-03-01 16:34:08</td>\n",
       "      <td>wechat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09b70fcd83</td>\n",
       "      <td>59f84cf342</td>\n",
       "      <td>2018-03-01 22:20:35</td>\n",
       "      <td>wechat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sku_ID     user_ID         request_time channel    order_ID  \\\n",
       "0  a234e08c57  4c3d6d10c2  2018-03-01 23:57:53  wechat         NaN   \n",
       "1  6449e1fd87           -  2018-03-01 16:13:48  wechat         NaN   \n",
       "2  09b70fcd83  2791ec4485  2018-03-01 22:10:51  wechat  e4874e2a00   \n",
       "3  09b70fcd83  eb0718c1c9  2018-03-01 16:34:08  wechat         NaN   \n",
       "4  09b70fcd83  59f84cf342  2018-03-01 22:20:35  wechat         NaN   \n",
       "\n",
       "   order_date           order_time  quantity  type promise  ...  \\\n",
       "0         NaN                  NaN       NaN   NaN     NaN  ...   \n",
       "1         NaN                  NaN       NaN   NaN     NaN  ...   \n",
       "2  2018-03-01  2018-03-01 14:08:33       1.0   2.0       -  ...   \n",
       "3         NaN                  NaN       NaN   NaN     NaN  ...   \n",
       "4         NaN                  NaN       NaN   NaN     NaN  ...   \n",
       "\n",
       "   quantity_discount_per_unit  bundle_discount_per_unit  \\\n",
       "0                         NaN                       NaN   \n",
       "1                         NaN                       NaN   \n",
       "2                         0.0                       0.0   \n",
       "3                         NaN                       NaN   \n",
       "4                         NaN                       NaN   \n",
       "\n",
       "   coupon_discount_per_unit  gift_item  dc_ori  dc_des  click_week  \\\n",
       "0                       NaN        NaN     NaN     NaN           4   \n",
       "1                       NaN        NaN     NaN     NaN           4   \n",
       "2                       0.0        0.0    24.0    40.0           4   \n",
       "3                       NaN        NaN     NaN     NaN           4   \n",
       "4                       NaN        NaN     NaN     NaN           4   \n",
       "\n",
       "   click_hour  order_week  order_hour  \n",
       "0          23         NaN         NaN  \n",
       "1          16         NaN         NaN  \n",
       "2          22         4.0        14.0  \n",
       "3          16         NaN         NaN  \n",
       "4          22         NaN         NaN  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clor.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6521226a0ad78ebb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:25:52.819566700Z",
     "start_time": "2024-03-14T13:25:14.653971100Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "clor['request_time'] = pd.to_datetime(clor['request_time'])\n",
    "clor['click_day'] = clor['request_time'].apply(lambda x: str(x.day))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "748ad25199d91b4e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:25:52.835002600Z",
     "start_time": "2024-03-14T13:25:52.819566700Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sku_ID                                 a234e08c57\n",
       "user_ID                                4c3d6d10c2\n",
       "request_time                  2018-03-01 23:57:53\n",
       "channel                                    wechat\n",
       "order_ID                                      NaN\n",
       "order_date                                    NaN\n",
       "order_time                                    NaN\n",
       "quantity                                      NaN\n",
       "type                                          NaN\n",
       "promise                                       NaN\n",
       "original_unit_price                           NaN\n",
       "final_unit_price                              NaN\n",
       "direct_discount_per_unit                      NaN\n",
       "quantity_discount_per_unit                    NaN\n",
       "bundle_discount_per_unit                      NaN\n",
       "coupon_discount_per_unit                      NaN\n",
       "gift_item                                     NaN\n",
       "dc_ori                                        NaN\n",
       "dc_des                                        NaN\n",
       "click_week                                      4\n",
       "click_hour                                     23\n",
       "order_week                                    NaN\n",
       "order_hour                                    NaN\n",
       "click_day                                       1\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clor.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8cdc31be471e474a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:26:00.690610500Z",
     "start_time": "2024-03-14T13:25:52.835002600Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "clor['order_date'] = pd.to_datetime(clor['order_date'])\n",
    "clor['order_day'] = clor['order_date'].dt.strftime('%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f0b6440fa8c634f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:26:10.122977700Z",
     "start_time": "2024-03-14T13:26:00.681102500Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "user_click_day = clor.groupby('click_day')['user_ID'].nunique()\n",
    "user_click_day = user_click_day.to_frame().reset_index()\n",
    "user_click_day.columns = ['click_day', 'user_click_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6da0611e84f98a91",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:26:18.844575200Z",
     "start_time": "2024-03-14T13:26:10.125980300Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "user_order_day = clor.groupby('order_day')['user_ID'].nunique()\n",
    "user_order_day = user_order_day.to_frame().reset_index()\n",
    "user_order_day.columns = ['order_day', 'user_order_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2a0a4c261742907f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:26:18.861026200Z",
     "start_time": "2024-03-14T13:26:18.844575200Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>click_day</th>\n",
       "      <th>user_click_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>179413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>100082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>100923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>114903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>118868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>111627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>123236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>107812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>109032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18</td>\n",
       "      <td>103738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19</td>\n",
       "      <td>116945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>109798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20</td>\n",
       "      <td>111866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>21</td>\n",
       "      <td>106078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>22</td>\n",
       "      <td>110240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>23</td>\n",
       "      <td>115297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>24</td>\n",
       "      <td>119822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>25</td>\n",
       "      <td>143021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>26</td>\n",
       "      <td>138441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>27</td>\n",
       "      <td>161217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>28</td>\n",
       "      <td>190355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>29</td>\n",
       "      <td>173677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>122404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30</td>\n",
       "      <td>160344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>31</td>\n",
       "      <td>154011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4</td>\n",
       "      <td>126407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5</td>\n",
       "      <td>137946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6</td>\n",
       "      <td>137301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7</td>\n",
       "      <td>172923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8</td>\n",
       "      <td>186438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>9</td>\n",
       "      <td>136542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   click_day  user_click_num\n",
       "0          1          179413\n",
       "1         10          100082\n",
       "2         11          100923\n",
       "3         12          114903\n",
       "4         13          118868\n",
       "5         14          111627\n",
       "6         15          123236\n",
       "7         16          107812\n",
       "8         17          109032\n",
       "9         18          103738\n",
       "10        19          116945\n",
       "11         2          109798\n",
       "12        20          111866\n",
       "13        21          106078\n",
       "14        22          110240\n",
       "15        23          115297\n",
       "16        24          119822\n",
       "17        25          143021\n",
       "18        26          138441\n",
       "19        27          161217\n",
       "20        28          190355\n",
       "21        29          173677\n",
       "22         3          122404\n",
       "23        30          160344\n",
       "24        31          154011\n",
       "25         4          126407\n",
       "26         5          137946\n",
       "27         6          137301\n",
       "28         7          172923\n",
       "29         8          186438\n",
       "30         9          136542"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_click_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "52f9f856e7eb12ed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:26:19.112680300Z",
     "start_time": "2024-03-14T13:26:18.862642Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 条形宽度\n",
    "bar_width = 0.2\n",
    "# 透明度\n",
    "opacity = 0.4\n",
    "\n",
    "# 计算每个条形图的位置\n",
    "positions_order = np.arange(len(user_order_day['order_day']))\n",
    "positions_click = [x + bar_width for x in positions_order]\n",
    "\n",
    "# 星期-下单，使用 positions_order 调整位置\n",
    "plt.bar(positions_order, user_order_day['user_order_num'], bar_width, alpha=opacity, color='c', label='Order')\n",
    "\n",
    "# 星期-点击，使用 positions_click 调整位置\n",
    "plt.bar(positions_click, user_click_day['user_click_num'], bar_width, alpha=opacity, color='r', label='Click')\n",
    "\n",
    "# 添加折线图\n",
    "plt.plot(positions_order, user_order_day['user_order_num'], color='b', marker='o', label='Order')\n",
    "plt.plot(positions_click, user_click_day['user_click_num'], color='y', marker='x', label='Click')\n",
    "\n",
    "# 设置 x 轴标签和标题\n",
    "plt.xlabel('Day in Month')\n",
    "plt.ylabel('Users Number')\n",
    "plt.title('A Week Purchase Table with Trends')\n",
    "\n",
    "# 设置 x 轴刻度标签位置和标签内容\n",
    "plt.xticks([p + bar_width / 2 for p in positions_order], user_order_day['order_day'], rotation=45)\n",
    "\n",
    "# 其他设置\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "78146e4507e5e494",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:26:19.188182700Z",
     "start_time": "2024-03-14T13:26:19.110163600Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([                          'NaT', '2018-03-01T00:00:00.000000000',\n",
       "       '2018-03-02T00:00:00.000000000', '2018-03-03T00:00:00.000000000',\n",
       "       '2018-03-04T00:00:00.000000000', '2018-03-30T00:00:00.000000000',\n",
       "       '2018-03-24T00:00:00.000000000', '2018-03-10T00:00:00.000000000',\n",
       "       '2018-03-05T00:00:00.000000000', '2018-03-23T00:00:00.000000000',\n",
       "       '2018-03-12T00:00:00.000000000', '2018-03-16T00:00:00.000000000',\n",
       "       '2018-03-07T00:00:00.000000000', '2018-03-22T00:00:00.000000000',\n",
       "       '2018-03-08T00:00:00.000000000', '2018-03-14T00:00:00.000000000',\n",
       "       '2018-03-20T00:00:00.000000000', '2018-03-06T00:00:00.000000000',\n",
       "       '2018-03-09T00:00:00.000000000', '2018-03-19T00:00:00.000000000',\n",
       "       '2018-03-18T00:00:00.000000000', '2018-03-26T00:00:00.000000000',\n",
       "       '2018-03-28T00:00:00.000000000', '2018-03-13T00:00:00.000000000',\n",
       "       '2018-03-15T00:00:00.000000000', '2018-03-25T00:00:00.000000000',\n",
       "       '2018-03-11T00:00:00.000000000', '2018-03-17T00:00:00.000000000',\n",
       "       '2018-03-27T00:00:00.000000000', '2018-03-21T00:00:00.000000000',\n",
       "       '2018-03-29T00:00:00.000000000', '2018-03-31T00:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clor['order_date'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cb580f4ca0bbc5db",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:26:19.208660300Z",
     "start_time": "2024-03-14T13:26:19.189183600Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sku_ID                                 a234e08c57\n",
       "user_ID                                4c3d6d10c2\n",
       "request_time                  2018-03-01 23:57:53\n",
       "channel                                    wechat\n",
       "order_ID                                      NaN\n",
       "order_date                                    NaT\n",
       "order_time                                    NaN\n",
       "quantity                                      NaN\n",
       "type                                          NaN\n",
       "promise                                       NaN\n",
       "original_unit_price                           NaN\n",
       "final_unit_price                              NaN\n",
       "direct_discount_per_unit                      NaN\n",
       "quantity_discount_per_unit                    NaN\n",
       "bundle_discount_per_unit                      NaN\n",
       "coupon_discount_per_unit                      NaN\n",
       "gift_item                                     NaN\n",
       "dc_ori                                        NaN\n",
       "dc_des                                        NaN\n",
       "click_week                                      4\n",
       "click_hour                                     23\n",
       "order_week                                    NaN\n",
       "order_hour                                    NaN\n",
       "click_day                                       1\n",
       "order_day                                     NaN\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clor.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "69071f880f1e11cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:27:50.291031600Z",
     "start_time": "2024-03-14T13:26:19.203643900Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "clor.to_csv('CL_OR.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32cbe8313fb8ee97",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## 查看特定用户对特定商品的轨迹"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "546ae4de083a359c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:28:22.514196400Z",
     "start_time": "2024-03-14T13:27:50.291031600Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "clor = pd.read_csv('./CL_OR.csv')\n",
    "users = pd.read_csv('./user_expand.csv')\n",
    "skus = pd.read_csv('./sku_expand.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f9b667f6e93edfac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:28:22.529331200Z",
     "start_time": "2024-03-14T13:28:22.515206Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_ID              000089d6a6\n",
       "user_level                    1\n",
       "first_order_month       2017-08\n",
       "plus                          0\n",
       "gender                        F\n",
       "age                           3\n",
       "marital_status                S\n",
       "education                     3\n",
       "city_level                    4\n",
       "purchase_power                3\n",
       "is_click                   10.0\n",
       "is_order                    8.0\n",
       "order_click_ratio           0.8\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d5143f4da0e95577",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:28:22.559708100Z",
     "start_time": "2024-03-14T13:28:22.528330400Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sku_ID               a234e08c57\n",
       "type                          1\n",
       "brand_ID             c3ab4bf4d9\n",
       "attribute1                  3.0\n",
       "attribute2                 60.0\n",
       "activate_date               NaN\n",
       "deactivate_date             NaN\n",
       "is_click                    644\n",
       "is_order                     74\n",
       "order_click_ratio      0.114907\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skus.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bebf4fed499a6f97",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:28:22.561215800Z",
     "start_time": "2024-03-14T13:28:22.543957300Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                                      0\n",
       "sku_ID                                 a234e08c57\n",
       "user_ID                                4c3d6d10c2\n",
       "request_time                  2018-03-01 23:57:53\n",
       "channel                                    wechat\n",
       "order_ID                                      NaN\n",
       "order_date                                    NaN\n",
       "order_time                                    NaN\n",
       "quantity                                      NaN\n",
       "type                                          NaN\n",
       "promise                                       NaN\n",
       "original_unit_price                           NaN\n",
       "final_unit_price                              NaN\n",
       "direct_discount_per_unit                      NaN\n",
       "quantity_discount_per_unit                    NaN\n",
       "bundle_discount_per_unit                      NaN\n",
       "coupon_discount_per_unit                      NaN\n",
       "gift_item                                     NaN\n",
       "dc_ori                                        NaN\n",
       "dc_des                                        NaN\n",
       "click_week                                      4\n",
       "click_hour                                     23\n",
       "order_week                                    NaN\n",
       "order_hour                                    NaN\n",
       "click_day                                       1\n",
       "order_day                                     NaN\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clor.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "73f6a0def3e44341",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-14T13:28:22.582091500Z",
     "start_time": "2024-03-14T13:28:22.560157600Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": []
  }
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